This package contains a least trimmed squares algorithm written in Python3 and modified for geophysical array processing. An extensive collection of helper functions is also included. These codes are referenced in
Bishop, J.W., Fee, D., & Szuberla, C. A. L., (2020). Improved infrasound array processing with robust estimators, Geophys. J. Int., 221(3) p. 2058-2074 doi: https://doi.org/10.1093/gji/ggaa110
Documentation for this package can be found here. A broader set of geophysical array processing codes are available here, which utilizes this package as the default (and preferred) array processing algorithm.
Infrasonic and seismic array processing often relies on the plane wave assumption. With this assumption, inter-element travel times can be regressed over station (co-)array coordinates to determine an optimal back-azimuth and velocity for waves crossing the array. Station errors such as digitizer timing issues, reversed polarity, and flat channels can manifest as apparent deviations from the plane wave assumption as travel time outliers. Additionally, physical deviations from the plane wave assumption also appear as travel time outliers. This project identifies these outliers from infrasound (and seismic) arrays through the least trimmed squares robust regression technique. Our python implementation uses the FAST_LTS algorithm of Rousseeuw and Van Driessen (2006). Please see Bishop et al. (2020) for processing examples at arrays from the International Monitoring System and Alaska Volcano Observatory.
We recommend using conda and creating a new conda environment such as:
conda create -n uafinfra -c conda-forge python=3 obspy numba
Information on conda environments (and more) is available here.
The package numba is a new dependency to pull request 23. If you have a previous uafinfra environment, you may need to install the numba package with
conda install --name uafinfra numba
After setting up the conda environment, install the package by running the terminal commands:
conda activate uafinfra git clone https://github.com/uafgeotools/lts_array cd lts_array pip install -e .
This set of commands activates the uafinfra conda environment. The final command installs the package in “editable” mode, which means that you can update it with a simple git pull in your local repository. This install command only needs to be run once.
and their dependencies.
To access the functions in this package, use the following line (for example):
>> python import lts_array as lts_array
See the included example.py file. The code now automatically calculates uncertainty estimates using the slowness ellipse method of Szuberla and Olson (2004). User notes and more information on uncertainty quantification can be found here.
If you use this code for array processing, we ask that you cite the following papers:
- Bishop, J.W., Fee, D., & Szuberla, C. A. L., (2020). Improved infrasound array processing with robust estimators, Geophys. J. Int., 221(3) p. 2058-2074 doi: https://doi.org/10.1093/gji/ggaa110
- Rousseeuw, P. J. & Van Driessen, K., 2006. Computing LTS regression for large data sets, Data Mining and Knowledge Discovery, 12(1), 29-45 doi: https://doi.org/10.1007/s10618-005-0024-4
- Szuberla, C.A.L. & Olson, J.V., 2004. Uncertainties associated with parameter estimation in atmospheric infrasound arrays, J. Acoust. Soc. Am., 115(1), 253–258. doi: https://doi.org/10.1121/1.1635407
MIT (c)
This work was made possible through support provided by the Defense Threat Reduction Agency Nuclear Arms Control Technology program under contract HDTRA1-17-C-0031. Distribution Statement A: Approved for public release; distribution is unlimited.